from torch import nn
try:
    from torch.hub import load_state_dict_from_url
except ImportError:
    from torch.utils.model_zoo import load_url as load_state_dict_from_url


model_urls = {
    'mobilenet_v2': 'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth',
}

CONV = {'2d': nn.Conv2d, '3d': nn.Conv3d}
BATCH_NORM = {'2d': nn.BatchNorm2d, '3d': nn.BatchNorm3d}


class ConvBNReLU(nn.Sequential):
    

    def __init__(self, in_planes, out_planes, mode='2d', kernel_size=3, stride=1, groups=1):
        padding = (kernel_size - 1) // 2
        mode = mode.lower()
        super(ConvBNReLU, self).__init__(
            CONV[mode](in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
            BATCH_NORM[mode](out_planes),
            nn.ReLU6(inplace=True)
        )


class InvertedResidual(nn.Module):
    

    def __init__(self, inp, oup, stride, expand_ratio, mode='2d'):
        super(InvertedResidual, self).__init__()
        mode = mode.lower()
        self.stride = stride
        assert stride in [1, 2]

        hidden_dim = int(round(inp * expand_ratio))
        self.use_res_connect = self.stride == 1 and inp == oup

        layers = []
        if expand_ratio != 1:
            # pw
            layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1, mode=mode))
        layers.extend([
            # dw
            ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim, mode=mode),
            # pw-linear
            CONV[mode](hidden_dim, oup, 1, 1, 0, bias=False),
            BATCH_NORM[mode](oup),
        ])
        self.conv = nn.Sequential(*layers)

    
    def forward(self, x):
        if self.use_res_connect:
            return x + self.conv(x)
        else:
            return self.conv(x)


class MobileNetV2(nn.Module):
    

    def __init__(self, num_classes=1000, width_mult=1.0, mode='2d'):
        super(MobileNetV2, self).__init__()
        
        # settings
        self.input_range = [0, 1]
        self.mean = [0.5, 0.5, 0.5]
        self.std  = [0.5, 0.5, 0.5]
        mode = mode.lower()
        self.mode = mode
        block = InvertedResidual
        input_channel = 32
        last_channel = 1280
        inverted_residual_setting = [
            # t, c, n, s
            [1, 16, 1, 1],
            [6, 24, 2, 2],
            [6, 32, 3, 2],
            [6, 64, 4, 2],
            [6, 96, 3, 1],
            [6, 160, 3, 2],
            [6, 320, 1, 1],
        ]

        # building first layer
        input_channel = int(input_channel * width_mult)
        self.last_channel = int(last_channel * max(1.0, width_mult))
        features = [ConvBNReLU(3, input_channel, stride=2, mode=mode)]
        # building inverted residual blocks
        for t, c, n, s in inverted_residual_setting:
            output_channel = int(c * width_mult)
            for i in range(n):
                stride = s if i == 0 else 1
                features.append(block(input_channel, output_channel, stride, expand_ratio=t, mode=mode))
                input_channel = output_channel
        # building last several layers
        features.append(ConvBNReLU(input_channel, self.last_channel, kernel_size=1, mode=mode))
        # make it nn.Sequential
        self.features = nn.Sequential(*features)

        # building classifier
        self.classifier = nn.Sequential(
            nn.Dropout(0.2),
            nn.Linear(self.last_channel, num_classes),
        )

        # weight initialization
        for m in self.modules():
            if isinstance(m, CONV[mode]):
                nn.init.kaiming_normal_(m.weight, mode='fan_out')
                if m.bias is not None:
                    nn.init.zeros_(m.bias)
            elif isinstance(m, BATCH_NORM[mode]):
                nn.init.ones_(m.weight)
                nn.init.zeros_(m.bias)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.zeros_(m.bias)


    def forward(self, x):
        x = self.features(x)
        if self.mode == '3d':
            x = x.mean([2, 3, 4])
        elif self.mode == '2d':
            x = x.mean([2, 3])
        x = self.classifier(x)
        return x


def _mobilenetv2(pretrained=None, progress=True, **kwargs):
    """
    Constructs a MobileNetV2 architecture from
    `"MobileNetV2: Inverted Residuals and Linear Bottlenecks" <https://arxiv.org/abs/1801.04381>`_.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    model = MobileNetV2(**kwargs)
    if pretrained:
        state_dict = load_state_dict_from_url(model_urls['mobilenet_v2'],
                                              progress=progress)
        model.load_state_dict(state_dict)
    return model


def MobileNet3D(pretrained=None, progress=True, **kwargs):
    model = MobileNetV2(mode='3d', **kwargs)
    if pretrained:
        weights_2d = _mobilenetv2(pretrained=True).state_dict() 
        for w in list(weights_2d.keys()):
            try:
                model.state_dict()[w].data.copy_(weights_2d[w])
            except RuntimeError:
                tmp_2d_weights = weights_2d[w].unsqueeze(2).repeat(1,1,model.state_dict()[w].shape[-1],1,1)
                model.state_dict()[w].data.copy_(tmp_2d_weights / tmp_2d_weights.shape[-1])
    # Change temporal stride
    model.features[2].conv[1][0].stride = (1,2,2)
    model.features[4].conv[1][0].stride = (1,2,2)
    return model

